Optimal Amnesic Probabilistic Automata or How to Learn and Classify Proteins in Linear Time and Space

@article{Apostolico2000OptimalAP,
  title={Optimal Amnesic Probabilistic Automata or How to Learn and Classify Proteins in Linear Time and Space},
  author={Alberto Apostolico and Gill Bejerano},
  journal={Journal of computational biology : a journal of computational molecular cell biology},
  year={2000},
  volume={7 3-4},
  pages={381-93}
}
Statistical modeling of sequences is a central paradigm of machine learning that finds multiple uses in computational molecular biology and many other domains. The probabilistic automata typically built in these contexts are subtended by uniform, fixed-memory Markov models. In practice, such automata tend to be unnecessarily bulky and computationally imposing both during their synthesis and use. Recently, D. Ron, Y. Singer, and N. Tishby built much more compact, tree-shaped variants of… CONTINUE READING
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